Due to the state-of-the-art performance of StyleGAN[1]} and promising theoretical support, many works are based on it, especially focusing on fine-grained image editing.
In StyleGAN[1]}, users have no access to control the attributes generation outcome of the synthetic images.
Among these, Wang et al. [3]} proposes to extract semantic label vectors from the text input, and then use the differentiated results between the semantic label vectors and the image classification vectors to guide the feature modification of the randomly generated images.
The experimental results show the method succeeds in generating images with desired features with good diversity, but the percentage of an image generation batch which correctly shows all of the desired features is low.
The reason for this is because the low accuracy of the multi-attribute classifier fails to recognise the features that the input images have.
Besides, although the work uses StyleGAN[1]} as the backbone, the image editing is still conducted by modifying the random noise vectors sampled from the normal distribution, which means the work did not take any advantage of the disentanglement ability of StyleGAN[1]}.
At the same time, there have been several works focusing on it.
Among these works, Härkönen et al. [6]} proposes to identify important latent directions based on the Principal Components Analysis (PCA) of the latent space vectors.
To do so, they sampled over 10000 latent space vectors and then computed a PCA on these vectors, which gives a basis \(\mathcal {V}\)  for \(\mathcal {W}\) .
Given a new image defined by \(w\) , it can be edited by varying a combination of PCA coordinates of the basis \(\mathcal {V}\) .
Shen et al. [7]} noted that this method has some defects.
First, the sampled vectors need to be huge in number or the vectors will not fully cover the multi-modal data distribution.
Second, the enormous number of sampling operations is time-consuming.
Therefore, they propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights of the official StyleGAN[1]} models.
Besides, compared to other methods, the method is more robust in finding the principle feature directions because it ameliorates the influence of the bias on the sampling data.
